System and method for vehicle collision mitigation with vulnerable road user context sensing

ABSTRACT

A system and method for vehicle collision mitigation with vulnerable road user context sensing. The system and method include determining one or more biosignal parameters and one or more physical movement parameters associated with a vulnerable road user (VRU). The system and method also include determining a context of the VRU based on the one or more biosignal parameters and one or more physical movement parameters associated with the VRU. Additionally, the system and method include determining one or more physical movement parameters associated with a vehicle. The system and method further include estimating a probability of collision between the VRU and the vehicle. The system and method also include providing a human machine interface output response based on the estimation of probability of collision between the VRU and the vehicle.

This application is a continuation of, and claims priority to, U.S.patent application Ser. No. 14/700,223 filed on Apr. 30, 2015, which isexpressly incorporated herein by reference.

BACKGROUND

Wearable computing devices and other portable computers can beintegrated across a wide variety of domains and fields for dataacquisition. A person that is utilizing a road for purposes other thandriving (e.g., walking, running, biking) can be classified as avulnerable road user. Generally, vulnerable road users are at a greaterrisk than vehicle occupants succumbing to injury or fatality in an eventof a traffic collision with a vehicle. Children and elderly people areparticularly vulnerable as having a higher propensity of being involvedin a traffic collision as their physical and mental skills are eithernot fully developed or they are particularly fragile. Vulnerable roadusers may not be aware of vehicles that are located on the roadway.Additionally, drivers of vehicles may not be aware of vulnerable roadusers that are located on the roadway. For example, vehicles may beapproaching the location in which the vulnerable road user may approachwithout the driver of the vehicle being aware of the presence of thevulnerable road user, and the vulnerable road user being aware of thepresence of the vehicle.

BRIEF DESCRIPTION

According to one aspect, a computer-implemented method for vehiclecollision mitigation with vulnerable road user context sensing includesdetermining one or more biosignal parameters and one or more physicalmovement parameters associated with a vulnerable road user (VRU). Themethod also includes determining a context of the VRU based on the oneor more biosignal parameters and one or more physical movementparameters associated with the VRU. Additionally, the method includesdetermining one or more physical movement parameters associated with avehicle. The method further includes estimating a probability ofcollision between the VRU and the vehicle based on the context of theVRU, the one or more physical movement parameters associated with theVRU, and the one or more physical movement parameters associated withthe vehicle. The method also includes providing a human machineinterface output response based on the estimation of probability ofcollision between the VRU and the vehicle, wherein the human machineinterface output response is provided on at least one of the following:a head unit of the vehicle, a wearable computing device, and a portabledevice.

According to a further aspect, a system for providing vehicle collisionmitigation with vulnerable road user context sensing includes a VRUvehicle collision mitigation application that is executed on at leastone of: a wearable computing device worn by and/or in possession of avulnerable road user (VRU), a portable device in possession of the VRU,and a head unit of a vehicle. The wearable computing device includesbiosignal sensors and physical signal sensors, the portable deviceincludes physical signal sensors, and the vehicle includes vehiclesensors. The system also includes a VRU bio-movement learning modulethat is included as a module of the VRU vehicle collision mitigationapplication that determines one or more biosignal parameters andphysical movement parameters associated with the VRU. Additionally, thesystem includes a VRU context determination module that is included as amodule of the VRU vehicle collision mitigation application thatdetermines a context of the VRU based on the one or more biosignalparameters and the physical movement parameters associated with the VRU.The system further includes a vehicle physical movement determinationmodule that is included as a module of the VRU vehicle collisionmitigation application that determines one or more physical movementparameters associated with the vehicle. The system also includes acollision probability estimation module that is included as a module ofthe VRU vehicle collision mitigation application that estimates aprobability of collision between the VRU and the vehicle based on thecontext of the VRU, the one or more physical movement parametersassociated with the VRU, and the one or more physical movementparameters associated with the vehicle. In addition, the system includesa HMI control module that is included as a module of the VRU vehiclecollision mitigation application that provides a human machine interfaceoutput response based on the estimation of probability of collisionbetween the VRU and the vehicle. The human machine interface outputresponse is provided on at least one of the following: the head unit ofthe vehicle, the wearable device, and the portable device.

According to still another aspect, a computer readable medium includinginstructions that when executed by a processor executes a method forvehicle collision mitigation with vulnerable road user context sensingthat includes determining one or more biosignal parameters and one ormore physical movement parameters associated with a vulnerable road user(VRU). The method also includes determining a context of the VRU basedon the one or more biosignal parameters and one or more physicalmovement parameters associated with the VRU. Additionally, the methodincludes determining one or more physical movement parameters associatedwith a vehicle. The method further includes estimating a probability ofcollision between the VRU and the vehicle based on the context of theVRU, the one or more physical movement parameters associated with theVRU, and the one or more physical movement parameters associated withthe vehicle. The method also includes providing a human machineinterface output response based on the estimation of probability ofcollision between the VRU and the vehicle, wherein the human machineinterface output response is provided on at least one of the following:a head unit of the vehicle, a wearable computing device, and a portabledevice.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic view of an operating environment for implementingsystems and methods for vehicle collision mitigation with vulnerableroad user context sensing according to an exemplary embodiment;

FIG. 2 is a process flow diagram of a method for vehicle collisionmitigation with vulnerable road user context sensing utilized by the VRUvehicle collision application from the operating environment of FIG. 1according to an exemplary embodiment;

FIG. 3 is a process flow diagram of a method for determining exercisethreshold values and velocity threshold values of a vulnerable road userduring a VRU contextual learning phase of the VRU vehicle collisionapplication from the operating environment of FIG. 1 according to anexemplary embodiment;

FIG. 4 is a process flow diagram of a method for determining a contextof a vulnerable road user during an execution mode of the VRU vehiclecollision application from the operating environment of FIG. 1 accordingto an exemplary embodiment;

FIG. 5A is a process flow diagram of a method for determining an overlapbetween the future expected positions of the vulnerable road user andthe vehicle during the execution phase of the VRU vehicle collisionapplication from the operating environment of FIG. 1 according to anexemplary embodiment;

FIG. 5B is an illustrative example of estimating an overlap between anexpected path of the vulnerable road user and an expected path of thevehicle based on the process flow diagram of FIG. 5A according to anexemplary embodiment; and

FIG. 6 is a process flow diagram of a method for estimating aprobability of collision between the vulnerable road user and thevehicle during the execution phase of the VRU vehicle collisionapplication from the operating environment of FIG. 1 according to anexemplary embodiment.

DETAILED DESCRIPTION

The following includes definitions of selected terms employed herein.The definitions include various examples and/or forms of components thatfall within the scope of a term and that can be used for implementation.The examples are not intended to be limiting.

A “bus”, as used herein, refers to an interconnected architecture thatis operably connected to other computer components inside a computer orbetween computers. The bus can transfer data between the computercomponents. The bus can be a memory bus, a memory controller, aperipheral bus, an external bus, a crossbar switch, and/or a local bus,among others. The bus can also be a vehicle bus that interconnectscomponents inside a vehicle using protocols such as Media OrientedSystems Transport (MOST), Controller Area network (CAN), LocalInterconnect Network (LIN), among others.

“Computer communication”, as used herein, refers to a communicationbetween two or more computing devices (e.g., computer, personal digitalassistant, cellular telephone, network device) and can be, for example,a network transfer, a file transfer, an applet transfer, an email, ahypertext transfer protocol (HTTP) transfer, and so on. A computercommunication can occur across, for example, a wireless system (e.g.,IEEE 802.11), an Ethernet system (e.g., IEEE 802.3), a token ring system(e.g., IEEE 802.5), a local area network (LAN), a wide area network(WAN), a point-to-point system, a circuit switching system, a packetswitching system, among others.

A “disk”, as used herein can be, for example, a magnetic disk drive, asolid state disk drive, a floppy disk drive, a tape drive, a Zip drive,a flash memory card, and/or a memory stick. Furthermore, the disk can bea CD-ROM (compact disk ROM), a CD recordable drive (CD-R drive), a CDrewritable drive (CD-RW drive), and/or a digital video ROM drive (DVDROM). The disk can store an operating system that controls or allocatesresources of a computing device.

A “database”, as used herein can refer to table, a set of tables, a setof data stores and/or methods for accessing and/or manipulating thosedata stores. Some databases can be incorporated with a disk as definedabove.

A “memory”, as used herein can include volatile memory and/ornon-volatile memory. Non-volatile memory can include, for example, ROM(read only memory), PROM (programmable read only memory), EPROM(erasable PROM), and EEPROM (electrically erasable PROM). Volatilememory can include, for example, RAM (random access memory), synchronousRAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double datarate SDRAM (DDR SDRAM), and direct RAM bus RAM (DRRAM). The memory canstore an operating system that controls or allocates resources of acomputing device.

A “module”, as used herein, includes, but is not limited to,non-transitory computer readable medium that stores instructions,instructions in execution on a machine, hardware, firmware, software inexecution on a machine, and/or combinations of each to perform afunction(s) or an action(s), and/or to cause a function or action fromanother module, method, and/or system. A module may also include logic,a software controlled microprocessor, a discrete logic circuit, ananalog circuit, a digital circuit, a programmed logic device, a memorydevice containing executing instructions, logic gates, a combination ofgates, and/or other circuit components. Multiple modules may be combinedinto one module and single modules may be distributed among multiplemodules.

An “operable connection”, or a connection by which entities are“operably connected”, is one in which signals, physical communications,and/or logical communications can be sent and/or received. An operableconnection can include a wireless interface, a physical interface, adata interface and/or an electrical interface.

A “processor”, as used herein, processes signals and performs generalcomputing and arithmetic functions. Signals processed by the processorcan include digital signals, data signals, computer instructions,processor instructions, messages, a bit, a bit stream, or other meansthat can be received, transmitted and/or detected. Generally, theprocessor can be a variety of various processors including multiplesingle and multicore processors and co-processors and other multiplesingle and multicore processor and co-processor architectures. Theprocessor can include various modules to execute various functions.

A “portable device”, as used herein, is a computing device typicallyhaving a display screen with user input (e.g., touch, keyboard) and aprocessor for computing. Portable devices include, but are not limitedto, handheld devices, mobile devices, smart phones, laptops, tablets ande-readers. In some embodiments, a “portable device” could refer to aremote device that includes a processor for computing and/or acommunication interface for receiving and transmitting data remotely.

A “vehicle”, as used herein, refers to any moving vehicle that iscapable of carrying one or more human occupants and is powered by anyform of energy. The term “vehicle” includes, but is not limited to:cars, trucks, vans, minivans, SUVs, motorcycles, scooters, boats,go-karts, amusement ride cars, rail transport, personal watercraft, andaircraft. In some cases, a motor vehicle includes one or more engines.Further, the term “vehicle” can refer to an electric vehicle (EV) thatis capable of carrying one or more human occupants and is poweredentirely or partially by one or more electric motors powered by anelectric battery. The EV can include battery electric vehicles (BEV) andplug-in hybrid electric vehicles (PHEV). The term “vehicle” can alsorefer to an autonomous vehicle and/or self-driving vehicle powered byany form of energy. The autonomous vehicle may or may not carry one ormore human occupants. Further, the term “vehicle” can include vehiclesthat are automated or non-automated with pre-determined paths orfree-moving vehicles.

A “vehicle system”, as used herein can include, but is not limited to,any automatic or manual systems that can be used to enhance the vehicle,driving and/or safety. Exemplary vehicle systems include, but are notlimited to: an electronic stability control system, an anti-lock brakesystem, a brake assist system, an automatic brake prefill system, a lowspeed follow system, a cruise control system, a collision warningsystem, a collision mitigation braking system, an auto cruise controlsystem, a lane departure warning system, a blind spot indicator system,a lane keep assist system, a navigation system, a transmission system,brake pedal systems, an electronic power steering system, visual devices(e.g., camera systems, proximity sensor systems), a climate controlsystem, an electronic pretensioning system, among others.

A “wearable computing device”, as used herein can include, but is notlimited to, a computing device component (e.g., a processor) withcircuitry that can be worn by and/or in possession of a user. In otherwords, a wearable computing device is a computer that is subsumed intothe personal space of a user. Wearable computing devices can include adisplay and can include various sensors for sensing and determiningvarious parameters associated with a user. For example, location,motion, and biosignal (physiological) parameters, among others. Somewearable computing devices have user input and output functionality.Exemplary wearable computing devices can include, but are not limitedto, watches, glasses, clothing, gloves, hats, shirts, jewelry, rings,earrings necklaces, armbands, shoes, earbuds, headphones and personalwellness devices.

A “value” and “level”, as used herein can include, but is not limitedto, a numerical or other kind of value or level such as a percentage, anon-numerical value, a discrete state, a discrete value, a continuousvalue, among others. The term “value of X” or “level of X” as usedthroughout this detailed description and in the claims refers to anynumerical or other kind of value for distinguishing between two or morestates of X. For example, in some cases, the value or level of X may begiven as a percentage between 0% and 100%. In other cases, the value orlevel of X could be a value in the range between 1 and 10. In stillother cases, the value or level of X may not be a numerical value, butcould be associated with a given discrete state, such as “not X”,“slightly x”, “x”, “very x” and “extremely x”.

I. System Overview

Referring now to the drawings, wherein the showings are for purposes ofillustrating one or more exemplary embodiments and not for purposes oflimiting same, FIG. 1 is a schematic view of an operating environment100 for implementing systems and methods for vehicle collisionmitigation with vulnerable road user context sensing according to anexemplary embodiment. The components of environment 100, as well as thecomponents of other systems, hardware architectures, and softwarearchitectures discussed herein, can be combined, omitted, or organizedinto different architectures for various embodiments.

Generally, the environment 100 includes a vulnerable road user vehiclecollision mitigation application (VVCM application) 102 that is utilizedto predict behaviors (e.g., path of travel, rate of travel, overlapbetween travel paths) of a vulnerable road user (VRU) 104 and a vehicle106. The VVCM application 102 predicts the behaviors of the VRU 104 andthe vehicle 106 in order to warn the VRU 104 and a driver (not shown) ofthe vehicle 106 of a probability of collision between the VRU 104 andthe vehicle 106.

As described in more detail below, the VVCM application 102 can beexecuted on a head unit 108 of the vehicle 106, a wearable computingdevice 110 worn by and/or in possession of the VRU 104, a portabledevice 112 in possession of the VRU 104, and/or on an externally hostedcomputing infrastructure (not shown) that is accessed by the head unit108, the wearable computing device 110, and/or the portable device 112.Additionally, the VVCM application 102 can utilize components of thevehicle 106, the wearable computing device 110 worn and/or in possessionof the VRU 104, and the portable device 112 in possession of the VRU104.

In the illustrated embodiment of FIG. 1, the vehicle 106 can include avehicle computing device 114 (VCD) with provisions for processing,communicating and interacting with various components of the vehicle 106and other components of the environment 100. In one embodiment, the VCD114 can be implemented on the head unit 108, a navigation unit (notshown), an infotainment unit (not shown), an electronic control unit(not shown), among others. Generally, the VCD 114 includes a processor(not shown), a memory (not shown), a disk (not shown), and aninput/output (I/O) interface (not shown), which are each operablyconnected for computer communication via a bus (not shown. The I/Ointerface provides software and hardware to facilitate data input andoutput between the components of the VCD 114 and other components,networks, and data sources, of the environment 100. In some embodiments,the VCD 114 can control one or more vehicle systems and/or functions(e.g., engine control unit, acceleration, braking, etc.) to provide acollision avoidance capability. Specifically, as discussed below, theVCD 114 can control the engine control unit (not shown) and/or thebraking system to decelerate the speed of the vehicle 106 or stop thevehicle 106 based on an estimated probability of collision between thevehicle 106 and the VRU 104.

The VCD 114 is also operably connected for computer communication (e.g.,via the bus and/or the I/O interface) to the head unit 108. The headunit 108 can be connected to one or more display devices (not shown)(e.g., display screens), audio devices (not shown) (e.g., audio system,speakers), and haptic devices (not shown) (e.g., haptic steering wheel)that are utilized to provide a human machine interface (not shown) (HMI)to provide a driver of the vehicle 106 with various types ofinformation. Such information can include, but is not limited to, safetywarnings that are presented to the driver of the vehicle 106 to alertthe driver of possible safety issues. Specifically, as discussed in moredetail, the HMI can be presented by the head unit 108 via the displaydevices, audio devices, and/or haptic devices to provide warnings to thedriver that are controlled by the VVCM 102 application based on thepropensity and intensity of the predicted collision between the vehicle106 and the VRU 104.

In some embodiments, the head unit 108 can include a storage unit 116.In alternate embodiments, the storage unit 116 can be included as astand alone component of the vehicle 106. The storage unit 116 can storeone or more operating systems, applications, associated operating systemdata, application data, vehicle system and subsystem user interfacedata, and the like that are executed by the VCD 114 and/or the head unit108. As will be discussed in more detail below, the storage unit 116 canbe utilized by the VVCM application 102 to store one or more physicalmovement parameters that are associated with the vehicle 106 that arecollected during a VRU context learning phase of the VVCM application102.

The vehicle 106 can additionally include vehicle sensors 118 that cansense and provide the one or more physical movement parameters that areassociated with the vehicle 106 to be used by the VVCM application 102.It is understood that the vehicle sensors 118 can include, but are notlimited to, sensors associated with the vehicle systems and othersensors associated with the vehicle 106. Specific vehicle sensors 118can include, but are not limited to, vehicle speed sensors, vehicleacceleration sensors, vehicle angular velocity sensors, acceleratorpedal sensors, brake sensors, steering wheel angle sensors, vehiclelocational sensors (e.g., GPS), vehicle directional sensors (e.g.,vehicle compass), throttle position sensors, wheel sensors, anti-lockbrake sensors, camshaft sensors, among others. Other vehicle sensors 118can include, but are not limited to, cameras mounted to the interior orexterior of the vehicle 106, radar and laser sensors mounted to theexterior of the vehicle 106, etc. It is understood that the sensors canbe any type of sensor, for example, acoustic, electric, environmental,optical, imaging, light, pressure, force, thermal, temperature,proximity, among others.

In an exemplary embodiment, the vehicle sensors 118 are operable tosense a measurement of data associated with the physical movement of thevehicle 106 that includes, but is not limited to, a positional location(e.g., GNSS position) of the vehicle 106, an angular velocity andacceleration (hereinafter referred to as velocity) (e.g., real-timespeed) of the vehicle 106, and a directional orientation (e.g., heading)of the vehicle 106. The vehicle sensors 118 can provide the measurementof data associated with the physical movement of the vehicle 106 in theform of one or more data signals to the head unit 108, the wearablecomputing device 110, and/or the portable device 112. These data signalscan be converted into one or more physical movement parametersassociated with the vehicle 106 that can be provided to the VVCMapplication102. The physical movement parameters associated with thevehicle 106 can also be provided to vehicle systems and/or the VCD 114to generate other data metrics and parameters.

The vehicle 106 can additionally include a communications device 120that can communicate with one or more components of the operatingenvironment 100 and/or additional systems and components outside of theoperating environment 100. The communication device 120 of the vehicle106 can include, but is not limited to, one or more transceivers (notshown), one or more receivers (not shown), one or more transmitters (notshown), one or more antennas (not shown), and additional components (notshown) that can be utilized for wired and wireless computer connectionsand communications via various protocols, as discussed in detail above.For example, the communication device 120 can use a dedicated shortrange communication protocol (DSRC) that can be used to provide datatransfer to send/receive electronic signals with the wearable computingdevice 110 and/or the portable device 112 to be utilized by the VVCMapplication 102 over a respective vehicle to VRU communication network.The communication protocol can include, but is not limited to existingDSRC protocols, Wi-Fi, Bluetooth, etc.

As mentioned above, the operating environment 100 also includes thewearable computing device 110 that can be worn by and/or in possessionof the VRU 104 for sensing one or more parameters associated with theVRU 104. The one or more parameters associated with the VRU 104 that canbe sensed by the wearable computing device 110 can include, but are notlimited to, one or more biosignals parameters (e.g., physiological data)associated with the VRU 104 and one or more physical movement parametersassociated with the VRU 104 (e.g., velocity, directional location,positional location).

It is understood that the wearable computing device 110 can include acontrol unit 122 (e.g., a processor) with circuitry that can be worn byand/or in possession of the VRU 104. The control unit 122 can processand compute functions associated with the components of the wearablecomputing device 110. The wearable computing device 110 can additionallyinclude a communication device 124 that can communicate with one or morecomponents of the operating environment 100 and/or additional systemsand components outside of the operating environment 100. Thecommunication device 124 of the wearable computing device 110 caninclude, but is not limited to, one or more transceivers (not shown),one or more receivers (not shown), one or more transmitters (not shown),one or more antennas (not shown), and additional components (not shown)that can be used for wired and wireless computer connections andcommunications via various protocols, as discussed in detail above.

The communications device 124 can be additionally used by one or morecomponents of the wearable computing device 110 to communicate withcomponents that are residing externally from the wearable computingdevice 110. For example, the control unit 122 can utilize thecommunication device 124 to access the portable device 112, the headunit 108, and/or the external computing infrastructure in order toexecute one or more externally hosted applications, including the VVCMapplication 102. In one or more embodiments, the wearable computingdevice 110 can generally provide data to the head unit 108 of thevehicle 106 and/or the portable device 112, the data being associatedwith the VRU 104 wearing the wearable computing device 110. For example,the communication device 124 can use DSRC that can be used to providedata transfer to send/receive electronic signals with the vehicle 106and/or the portable device 112 to be utilized by the VVCM application102 over the respective vehicle to VRU communication network. Thecommunication protocol can include, but is not limited to existing DSRCprotocols, Wi-Fi, Bluetooth, etc.

The wearable computing device 110 can additionally include a storageunit 126. The storage unit 126 can store one or more operating systems,applications, associated operating system data, application data, andthe like that are executed by the control unit 122. As will be discussedin more detail below, the storage unit 126 can be accessed by the VVCMapplication 102 to store the one or more biosignal parameters and one ormore physical movement parameters associated with to the VRU 104 thatare collected during the VRU context learning phase of the VVCMapplication 102.

The wearable computing device 110 also includes a HMI output unit 128that can be capable of providing one or more HMI outputs to the VRU 104.The HMI output unit 128 can include, but is not limited to, one or morevisual devices (e.g., display screens), one or more audio devices (e.g.,speakers), and/or one or more haptic devices (e.g., tactile electronicdisplays). The HMI output unit 128 can provide the VRU 104 with varioustypes of information. Such information can include, but is not limitedto, safety warnings that are presented to the VRU 104 when one or moreapplications are executed on the wearable computing device 110.Specifically, as will be discussed in more detail, the HMI can bepresented by the HMI output unit 128 to provide warnings to the VRU 104based on an estimated probability of collision between the VRU 104 andthe vehicle 106 as provided by VVCM application 102.

The wearable device 110 can include biosignal sensors 130 for sensingand determining one or more biosignal parameters associated with the VRU104. In one embodiment, the biosignal sensors 130 can sensephysiological data and other data associated with the body andbiological system of the VRU 104. As discussed in more detail below, thebiosignal sensors 130 can provide one or more sensed VRU biosignalparameters associated with the VRU 104 to be evaluated by one or morecomponents of the VVCM application 102. The one or more VRU biosignalparameters can include, but are not limited to, heart information, suchas, heart rate, heart rate pattern, blood pressure, oxygen content,etc., brain information, such as, electroencephalogram (EEG)measurements, functional near infrared spectroscopy (fNIRS), functionalmagnetic resonance imaging (fMRI), etc., digestion information,respiration rate information, salivation information, perspirationinformation, pupil dilation information, body temperature, musclestrain, as well as other kinds of information related to the autonomicnervous system or other biological systems of the VRU 104. In someembodiments, the one or more VRU biosignal parameters can additionallyinclude behavioral information, for example, mouth movements, facialmovements, facial recognition, head movements, body movements, handpostures, hand placement, body posture, gesture recognition, amongothers.

In additional embodiments, the one or more biosignal parameters can becollected and provided to the vehicle 106 and/or the portable device 112to be stored respectively. As described in more detail below, the VVCMapplication 102 can access the one or more of the biosignal parametersto determine a plurality of exercise threshold values associated withthe VRU 104. The VRU's exercise threshold value(s) can include avalue(s) that are determined based on one or more VRU biosignalparameters that are collected and stored on the storage unit 126 of thewearable computing device 110 over the VRU context learning phase of theVVCM application 102. As will be described, during an execution phase ofthe VVCM 102 application, one or more of the VRU biosignal parameterscan be collected in real time in order to partially determine a contextof the VRU 104 (e.g., a description of the VRU as he/she is using theroad).

In addition to the one or more biosignal sensors 130, the wearablecomputing device 110 can additionally include one or more physicalsignal sensors 132. The one or more physical signal sensors 132 caninclude, but are not limited to, an accelerometer, a magnetometer, agyroscope, an ambient light sensor, a proximity sensor, a locationalsensor (e.g., GPS), a positional sensor, a directional sensor (e.g.,compass), and the like. Additionally, the one or more physical signalsensors 132 can include one or more cameras that can be accessed by theone or more applications executed and/or accessed on the wearablecomputing device 110.

In an exemplary embodiment, the one or more physical signal sensors 132can provide one or more physical movement parameters associated with theVRU 104 to be evaluated by one or more components of the VVCMapplication 102. As will be described in more detail below, the VVCMapplication 102 can extract velocity data from the one or more physicalmovement parameters associated with the VRU 104. During a predeterminedperiod of time, the physical movement parameters can be collected andstored on the storage unit 126 until the VVCM application 102 can createvelocity thresholds that are associated with the VRU 104. In additionalembodiments, the one or more VRU physical parameters can be collectedand provided to the vehicle 106 and/or the portable device 112 to bestored on one or more of the respective storage units 116, 128. Duringthe execution phase, the VVCM application 102 can receive the one ormore physical movement parameters associated with the VRU 104 (in realtime) to determine the velocity of the VRU 104. The velocity of the VRU104 is then compared to the one or more of the VRU's velocity thresholdvalues in order to partially determine the context of the VRU 104.

As mentioned above, the operating environment 100 also includes theportable device 112 that can be in possession of the VRU 104 to beutilized (e.g., used or held) by the VRU 104 for executing and/oraccessing one or more applications, web interfaces, and/or sensing oneor more physical movement parameters associated with the VRU 104. Theone or more physical movement parameters associated with the VRU 104that can be sensed by the portable device 112 can include, but are notlimited to, the velocity of the VRU 104 as he/she is moving, apositional location of the VRU 104, and a directional location of theVRU 104 (e.g., the heading of the VRU 104 as he/she is moving).

It is understood that the portable device 112 can include a control unit134 (e.g., a processor) with circuitry. The control unit 134 can processand compute functions associated with the components of the portabledevice 112. The portable device 112 can additionally include acommunication device 136 that can communicate with one or morecomponents of the operating environment 100 and/or additional systemsand components outside of the operating environment 100. Thecommunication device 136 can include, but is not limited to, one or moretransceivers (not shown), one or more receivers (not shown), one or moretransmitters (not shown), one or more antennas (not shown), andadditional components (not shown) that can be utilized for wired andwireless computer connections and communications via various protocols,as discussed in detail above.

The communications device 136 can be utilized by one or more componentsof the portable device 112 to communicate with components that areresiding externally from the wearable computing device 110. In oneembodiment, the control unit 134 can utilize the communication device136 to access the wearable computing device 110, the head unit 108,and/or external infrastructure in order to execute one or moreexternally hosted applications, including the VVCM application 102. Inone or more embodiments, the portable device 112 can generally providedata to the head unit 108 of the vehicle 106 and/or the wearablecomputing device 110, the data being associated with the VRU 104 inpossession of the portable device 112. For example, the communicationdevice 136 can communicate via DSRC to transfer data and send/receiveelectronic signals with the wearable computing device 110 and/or thevehicle 106 to be utilized by the VVCM application 102 over therespective vehicle to VRU communication network. The communicationprotocol can include, but is not limited to existing DSRC protocols,Wi-Fi, Bluetooth, etc.

The storage unit 138 can be utilized to store one or more operatingsystems, applications, associated operating system data, applicationdata, and the like that are executed by the control unit 134. As will bediscussed in more detail below, the storage unit 138 can be utilized bythe VVCM application 102 to store one or more physical movementparameters associated with the VRU 104 that is collected during apredetermined amount of time.

In one or more embodiments, the storage unit 138 can include profiledata associated with the VRU 104 that is accessed by one or moreapplications that are executed on the portable device 112. For example,profile data can be input by the VRU 104 or a third party associatedwith the VRU 104 and stored as a user profile within the storage unit138. Such profile data can be created during an initial setup of theportable device 112 and can include, but is not limited to, the user'sage, gender, and/or other demographic information. As described in moredetail below, the VVCM application 102 can access the profile data fromthe storage unit 138 as a factor in estimating the probability ofcollision between the VRU 104 and the vehicle 106.

The portable device 112 also includes a HMI output unit 140 that can becapable of providing one or more HMI outputs to the VRU 104. The HMIoutput unit 140 can include, but is not limited to, one or more visualdevices (e.g., display screens), one or more audio devices (e.g.,speakers), and/or one or more haptic devices (e.g., tactile electronicdisplays). The HMI output unit 140 can provide the VRU 104 with varioustypes of information. Such information can include, but is not limitedto, safety warnings that are presented to the VRU 104 when one or moreapplications are executed on the portable device 112. Specifically, asdiscussed in more detail, HMI output unit 140 can provide warnings tothe VRU 104 that are controlled by the VVCM application 102 based on theprobability of collision between the VRU 104 and the vehicle 106.

The portable device 112 can additionally include one or more physicalsignal sensors 142. The one or more physical signal sensors 142 caninclude, but are not limited to, an accelerometer, a magnetometer, agyroscope, an ambient light sensor, a proximity sensor, a locationalsensor (e.g., GPS), a positional sensor, a directional sensor (e.g.,compass), and the like. Additionally, the one or more physical signalsensors 142 can include one or more cameras that can be accessed by theone or more applications executed and/or accessed on the portable device112.

As will be discussed in more detail herein, in an exemplary embodiment,the one or more physical signal sensors 142 can provide the one or morephysical movement parameters associated with the VRU 104 to be evaluatedby one or more components of the VVCM application 102. In oneembodiment, if the VRU 104 is wearing and/or possessing the wearablecomputing device 110 and is possessing the portable device 112, the VVCMapplication 102 can aggregate the physical movement parametersassociated with the VRU 104 provided by the wearable computing device110 and the portable device 112. The VVCM application 102 can extractvelocity data from the aggregated physical movement parametersassociated with the VRU 104 in order to determine the VRU's velocitythreshold values. In another embodiment, the VVCM application 102 canextract velocity data from the one or more physical movement parametersassociated with the VRU 104 provided by the portable device 112independently from the physical movement parameters associated with theVRU 104 provided by the wearable computing device 110 in order for theVVCM application 102 to create the VRU's velocity thresholds.

II. The VVCM Application and Related Methods

The components of the VVCM application 102 will now be describedaccording to an exemplary embodiment and with reference to FIG. 1. In anexemplary embodiment, the VVCM application 102 can be stored on one ormore of the storage units 116, 126, 138 and executed by one or more ofthe head unit 108 of the vehicle 106, the wearable computing device 110,and/or the portable computing device 112. In another embodiment, theVVCM application 102 can be stored on the externally hosted computinginfrastructure and can be accessed by the communication devices 120,124, 136 to be executed by the head unit 108 of the vehicle 106, thewearable computing device 110, and/or the portable computing device 112.

In an exemplary embodiment, upon an initial execution of the VVCMapplication 102 by the VRU 104 via the wearable computing device 110and/or the portable device 112, a VRU contextual learning phase of theapplication 102 is initiated to evaluate one or more of the VRU'sbiosignal parameters and physical movement parameters associated withthe VRU 104 in order to create a context for the VRU 104. The context ofthe VRU 104 can include a designation of the VRU's usage of the road(e.g., walking, running, biking, passenger). Upon completion of the VRUcontextual learning phase of the application 102, the VVCM application102 can initiate an execution phase. During the execution phase of theapplication 102, the context of the VRU 104, physical movementparameters associated with the VRU 104 captured in real time, thephysical movement parameters associated with the vehicle 106 captured inreal time, and additional collision probability factors can be utilizedto estimate a probability of collision between the VRU 104 and thevehicle 106.

The VVCM application 102 can include a VRU bio-movement learning module144, a VRU context determination module 146, a vehicle physical movementdetermination module 148, a collision probability estimation module 150,and a HMI output control module 152. Methods related to one or moreprocesses that are executed by the modules 144-152 of the VVCMapplication 102 will also be described with reference to FIGS. 2-6.

FIG. 2 is a process flow diagram of a method 200 for vehicle collisionmitigation with vulnerable road user context sensing executed by theVVCM application 102 from the operating environment of FIG. 1 accordingto an exemplary embodiment. FIG. 2 will be described with reference tothe components of FIG. 1, though it is to be appreciated that the methodof FIG. 2 can be used with other systems/components. At block 202, themethod includes determining one or more biosignal parameters andphysical movement parameters associated with the VRU 104.

In an exemplary embodiment, the VRU bio-movement learning module 144 candetermine one or more biosignal parameters and physical movementparameters associated with the VRU 104 within the VRU contextuallearning phase of the VVCM application 102. Specifically, the VRUbio-movement learning module 144 can determine the exercise thresholdvalues and velocity threshold values associated with the VRU 104. Asdescribed below, within the execution phase of the application 102, theexercise threshold value(s) and velocity threshold value(s) are utilizedby the application 102 to classify the context of the VRU 104.

Specifically, the bio-movement learning module 340 can analyze the oneor more biosignal parameters associated with the VRU 104 provided by thebiosignal sensors 130 over the course of the VRU contextual learningphase of the application 102 in order to determine the exercisethreshold values associated with the VRU 104. The exercise thresholdvalues can include values that categorize one or more types ofactivities by a subset of one or more biosignal parameter rangesassociated with the VRU 104. In one embodiment, the exercise thresholdvalues can include, but are not limited to, a resting exercise thresholdvalue(s), an active exercise threshold value(s), and a hyperactiveexercise threshold value(s). However, other types of exercise thresholdvalues will be apparent.

Additionally, the bio-movement learning module 144 can analyze one ormore physical movement parameters associated with the VRU 104 asprovided by the physical signal sensors 132 of the wearable computingdevice 110 and/or the physical signal sensors 142 of the portable device112 over the course of the VRU contextual learning phase of theapplication 102. The physical movement parameters associated with theVRU 104 can be analyzed over the course of the VRU contextual learningphase of the application 102 in order to determine a plurality ofvelocity threshold values that pertain to the VRU 104. The velocitythreshold values can include values that categorize one or more rangesof velocity by a subset of one or more physical parameter rangesassociated with the VRU 104. The velocity threshold values can include,but are not limited to, a resting velocity threshold value, a walkingvelocity threshold value, and a running velocity threshold value.

The exercise threshold values are specific to each VRU 104 since theyaccount for the VRU's biosignal parameter(s) at specific velocities. Forinstance, an older VRU 104 may have a higher heart rate during walking,than a younger VRU 104. The velocity threshold values are specific toeach VRU 104 since they account for the VRU's average low velocity,medium velocity, and high velocity over the course of the contextuallearning phase of the application 102. For instance, an older VRU 104may have a slower average high velocity than a younger VRU 104.Therefore, the velocity threshold values ensure that each VRU's physicalmovement parameters are measured with respect to his/her velocity ofmovement.

Referring now to FIG. 3, a process flow diagram of a method 300 fordetermining exercise threshold values and velocity threshold values of aVRU 104 during a VRU contextual learning phase of the VVCM application102 from the operating environment of FIG. 1 according to an exemplaryembodiment. FIG. 3 will be described with reference to the components ofFIG. 1, though it is to be appreciated that the method of FIG. 3 can beused with other systems/components. At block 302, the method includesinitiating a VRU contextual learning phase of the VVCM application 102.

At block 304, the method includes receiving one or more biosignalparameters from a wearable computing device 110 worn and/or inpossession of a VRU 104. In one embodiment, the VRU bio-movementlearning module 144 can access the one or more biosignal sensors 130 ofthe wearable computing device 110 to retrieve one or more biosignalparameters that are associated with the VRU 104. As an illustrativeexample, the VRU bio-movement learning module 144 can receive a heartrate of the VRU 104 as a biosignal parameter from the wearable computingdevice 110 associated with the VRU 104. In some embodiments, thewearable computing device 110 is configured to transmit the one or morebiosignal parameters at a predetermined time interval. In otherembodiments, the bio-movement learning module 144 can access thebiosignal sensors 130 (locally or via the communication device 124) tosupply one or more biosignal parameters at the predetermined timeinterval. An exemplary predetermined time interval can be 50 ms at afrequency of about 20 Hz. In an exemplary embodiment, upon receiving theone or more biosignal parameters, the VRU bio-movement learning module144 can store the one or more biosignal parameters received from thebiosignal sensors 130 within the storage unit 126 of the wearablecomputing device 110 and/or the storage unit 138 of the portable device112 during the course of the VRU contextual learning phase of theapplication 102.

At block 306, the method includes receiving one or more physicalmovement parameters from a wearable computing device 110 worn and/or inpossession of a VRU 104 and/or a portable device 112 in possession ofthe VRU 104. In one embodiment, the VRU bio-movement learning module 144can access the one or more physical signal sensors 132 of the wearablecomputing device 110 to receive one or more physical movement parametersthat are associated with the VRU 104. In another embodiment, if the VRU104 is in possession of the portable device 112, the VRU bio-movementlearning module 144 can additionally access the physical signal sensors142 of the portable device 112 to retrieve the one or more physicalmovement parameters that are associated with the VRU 104. In oneembodiment, the physical movement parameters retrieved from the wearablecomputing device 110 can be aggregated with the physical movementparameters retrieved from the portable device 112 to extract velocitydata that is associated with the VRU 104. Specifically, as describedabove, the physical movement parameters can include, but are not limitedto, the positional location of the VRU 104, the directional location ofthe VRU 104, the velocity of the VRU 104, and the acceleration of theVRU 104. For instance, if the VRU 104 is wearing the wearable computingdevice 110 and carrying the potable device 112, the VRU bio-movementlearning module 144 can aggregate physical movement parameters retrievedfrom both devices 110, 112 to determine the velocity data of the VRU104.

At block 308, the method includes determining if a requisite amount ofbiosignal parameter data and physical movement parameter data have beenreceived to determine the VRU's exercise threshold values. One or morebiosignal parameters and physical movement parameters can continue to bereceived by the VRU bio-movement learning module 144 and stored for aperiod of time until a requisite amount of data is collected in orderfor the VRU bio-movement learning module 144 to determine the VRU'sexercise threshold values. If it is determined that the requisite amountof biosignal parameter data and physical movement parameter data has notbeen received to determine the VRU's exercise threshold values (at block308), the process returns to block 304, wherein the bio-movementlearning module 144 continues to receive one or more biosignalparameters from the wearable computing device 110 worn and/or inpossession of the VRU 104.

If it is determined that a requisite amount of biosignal parameter dataand physical movement parameter data has been received to determine theVRU's velocity threshold values (at block 308), at block 310, the methodincludes determining the VRU's exercise threshold values. In anexemplary embodiment, the VRU bio-movement learning module 144 canretrieve the biosignal parameters and physical movement parametersstored within the storage unit 126 of the wearable computing device 110and/or the storage unit 138 of the portable device 112. The VRUbio-movement learning module 144 can then extract data pertaining to thevelocity of the VRU 104 in order to be evaluated as velocity data of theVRU 104 over the course of the VRU contextual learning phase. Uponextracting the velocity data of the VRU 104 from the stored physicalmovement parameter data, the bio-movement learning module 144 canaggregate the biosignal parameter data and velocity data in order todetermine the VRU's exercise threshold values. Specifically, thebio-movement learning module 144 can aggregate the biosignal parameterdata and velocity data to determine the VRU's average biosignalparameter value(s) while the VRU 104 is moving at particular velocities.As an illustrative example, the VRU bio-movement learning module 144 candetermine the VRU's average heart rate (e.g., 100 bpm) that is retrievedwhile the VRU 104 is in movement at a particular velocity (3.1 miles perhour). The bio-movement learning module 144 can then classify an averageheart rate of the VRU 104 when the VRU 104 is moving at different ratesand ranges of velocity (e.g., 0 mph to 2 mph, 2.1 mph to 4.0 mph, 4.1mph to 7 mph, etc.) over the course of the VRU contextual learning phaseto determine the resting exercise threshold value(s), the activeexercise threshold value(s), and the hyperactive exercise thresholdvalue(s) associated with the VRU 104.

In an illustrative example, the resting exercise threshold value(s) caninclude a heart rate value that is higher than the average heart ratevalue of the VRU 104 when the VRU 104 is stationary. The active exercisethreshold value can include a heart rate value that is higher than theaverage heart rate value of the VRU 104 when the VRU 104 is moving at aslow or medium velocity (e.g., the VRU 104 is strolling, walking, orjogging). Additionally, the hyper-active exercise threshold value caninclude a heart rate value that is higher than the average heart ratevalue of the VRU 104 when the VRU 104 is moving at a high or very highvelocity (e.g., the VRU 104 is running or biking). As will be describedbelow, within the execution mode, the VVCM application 102 can determineone or more real time biosignal parameters associated with the VRU 104to determine if the one or more real time biosignal parameter valuesfall below or above any of the VRU's exercise threshold values. It isappreciated that other embodiments are apparent to determine the VRU'sexercise threshold values.

At block 312, the method includes determining if a requisite amount ofphysical movement parameter data has been received to determine theVRU'S velocity threshold values. The physical movement parameters cancontinue to be retrieved by the VRU bio-movement learning module 144 andstored for a period of time (within the storage unit 126 and/or storageunit 138) until a requisite amount of physical movement parameter datahas been received in order for the VRU bio-movement learning module 144to determine the VRU's velocity threshold values. If it is determinedthat a requisite amount of physical movement parameter data has not beenreceived to determine the VRU's velocity threshold values (at block312), the process returns to block 306, wherein the VRU bio-movementlearning module 144 continues to receive one or more physical movementparameters from the wearable computing device 110 worn and/or inpossession of the VRU 104 and/or the portable device 112 in possessionof the VRU 104.

If it is determined that a requisite amount of physical movementparameter data has been received to determine the VRU's velocitythreshold values (at block 312), at block 314, the method includesdetermining the VRU's velocity threshold values. In an exemplaryembodiment, during the VRU contextual learning phase, the VRUbio-movement learning module 144 can extract data pertaining to thevelocity of the VRU 104 from the physical movement parameter data storedwithin the storage unit 126 of the wearable computing device 110 and/orthe storage unit 138 of the portable device 112 (received and storedduring the course of the VRU contextual learning phase of theapplication 102). Upon extracting the velocity data of the VRU 104 fromthe stored physical movement parameter data, the bio-movement learningmodule 144 can determine average ranges of the VRU's velocity determinedduring the course of the VRU contextual learning phase to determine theVRU's velocity threshold values. Specifically, the bio-movement learningmodule 144 can evaluate the velocity data to determine a lower velocityrange, a medium velocity range, and a higher velocity range (e.g., 0 mphto 2 mph, 2.1 mph to 4.1 mph, 4.1 mph to 7 mph, etc.). The bio-movementlearning module 144 can classify an average velocity of the VRU 104 whenthe VRU 104 is moving at different rates and ranges of velocity over thecourse of the VRU contextual learning phase to determine the restingvelocity threshold value, the walking velocity threshold value, and therunning velocity threshold value.

As an illustrative example, the resting velocity threshold value caninclude a value that is higher than a zero velocity of the VRU 104 whenthe VRU 104 is stationary. The walking velocity threshold value caninclude a value that is higher than an average velocity of the VRU 104when the VRU is moving at a slow or medium velocity (e.g., the VRU 104is strolling, walking, or jogging). The running velocity threshold valuecan include value that is higher than an average velocity of the VRU 104when the VRU is moving at a high or very high velocity (e.g., the VRU104 is running or biking). As will be described below, within theexecution mode, the VVCM application 102 can determine one or more realtime biosignal parameters associated with the VRU 104 and one or morereal time physical movement parameters associated with the VRU 104 todetermine if the parameters are greater than or less than any of theVRU's velocity and exercise threshold values. It is appreciated thatother embodiments are apparent to determine the velocity thresholds ofthe VRU 104.

At block 316, the method includes completing the contextual learningphase of the VVCM application 102. In an exemplary embodiment, upondetermining the VRU's exercise threshold values and the VRU's velocitythreshold values, the VRU bio-movement leaning module 144 completes thecontextual learning phase of the VVCM application 102.

In some embodiments, the VVCM application 102 can restart the VRUcontextual learning phase after a predetermined period of time (e.g.,180 days) in order to dynamically evaluate one or more of the VRU'sbiosignal and physical movement parameters. For example, the VVCMapplication 102 can restart the VRU contextual learning phase in orderto update the VRU's exercise threshold values and the VRU's velocitythreshold values to account for changes with respect to one or more ofthe VRU's biosignal and velocity parameters over the course of time.

In additional embodiments, the VVCM application 102 can include a usersettings user interface that can be accessed by the VRU 104 via the HMIoutput unit 128 (e.g., display) of the wearable computing device 110and/or the HMI output unit 140 (e.g., display) of the portable device112. The user settings user interface can include a VRU contextuallearning phase initiation user input icon that can be inputted by theVRU 104 to reinitiate the VRU contextual learning phase wherein themethod 300 restarts at block 302 before or after the completion of theVRU contextual learning phase (at block 316). As an illustrativeexample, the VRU 104 may wish to restart the VRU contextual learningphase to evaluate/reevaluate one or more of the VRU's biosignal andphysical movement parameters during a period of time when the VRU 104 isinjured with a broken leg. In this illustrative example, the restartingof the VRU contextual learning phase can ensure that the VVCMapplication 102 accounts for the VRU's 102 elevated heart rate andslower velocity in such a circumstance in order to properly estimate theprobability of collision between the VRU 104 and the surrounding vehicle106.

Referring again to FIG. 2, upon determining the biosignal parameters andthe physical movement parameters associated with the VRU 104 (at block202), as discussed in detail above, at block 204, the method includesdetermining a context of the VRU 104. The context of the VRU 104 caninclude a designation of the VRU's usage of the road that can bepartially utilized to determine the propensity of collision between theVRU 104 and the vehicle 104 traveling on the road or a nearby roadwithin a predetermined (surrounding) distance. The context of the VRU104 can include but is not limited to a walking context, a runningcontext, a biking context, and a passenger context. Specifically, VRUcontext determination module 146 of the VVCM 104 can determine thecontext of the VRU 104 within the execution phase of the VVCMapplication 104 by comparing the one or more biosignal parameters andvelocity data associated with the VRU 104 determined in real time to theVRU's exercise threshold values and velocity threshold values determinedduring the VRU contextual learning phase of the VVCM application 102.

FIG. 4 is a process flow diagram of a method 400 for determining thecontext of the VRU 104 during an execution mode of the VVCM application102 from the operating environment of FIG. 1 according to an embodiment.FIG. 4 will be described with reference to the components of FIG. 1,though it is to be appreciated that the method of FIG. 4 can be usedwith other systems/components. Upon completion of the VRU contextuallearning phase (at block 316 of FIG. 3), as discussed above, the VVCMapplication 102 initializes the execution phase. Within the executionphase, the VVCM application 102 can determine one or more real timebiosignal parameters and physical movement parameters associated withthe VRU 104 with respect to one or more physical movement parametersassociated with the vehicle 106 in order to estimate a real timeprobability of collision between the VRU 104 and the vehicle 106.

At block 402, the method includes receiving one or more biosignalparameters and physical movement parameters in real time. In anexemplary embodiment, the VRU context determination module 146 canaccess the biosignal sensors 130 of the wearable computing device 110 toprovide one or more of the VRU's biosignal parameters in real timewithin a predetermined time interval. For instance, the VRU contextdetermination module 146 can access the biosignal sensors 130 to provideone or more of the VRU's biosignal parameters at a predetermined timeinterval of 50 ms at a frequency of about 20 Hz. Additionally, the VRUcontext determination module 146 can access the physical signal sensors132 of the wearable computing device 110 and/or the physical signalsensors 142 of the portable device 112 to provide the one or more of theVRU's physical movement parameters in real time. The VRU contextdetermination module 146 can then extract data pertaining to thevelocity of the VRU 104 in order to be evaluated as real time velocitydata of the VRU 104.

At block 404, the method includes determining if the VRU's velocity isgreater than the VRU's running velocity threshold value. Specifically,the VRU context determination module 146 can communicate with the VRUbio-movement learning module 144 to receive the VRU's running velocitythreshold value. Upon receiving the VRU's running velocity thresholdvalue, the VRU context determination module 146 can evaluate the realtime velocity (e.g., 2 mph) of the VRU 104 to determine if it is greaterthan the VRU's running velocity threshold value. If it is determinedthat the VRU's velocity is greater than the VRU's running velocitythreshold value (at block 404), at block 406, it is determined if theVRU biosignal parameter(s) is greater than the VRU's resting thresholdvalue(s). Specifically, the VRU context determination module 146 cancommunicate with the VRU bio-movement learning module 144 to receive theVRU's resting threshold value(s). Upon receiving the VRU's restingthreshold value(s), the VRU context determination module 146 canevaluate one or more real time biosignal parameters associated with theVRU 104 to determine if they are greater than the VRU's restingthreshold value(s) determined within the VRU context learning phase ofthe VVCM application 102. As an illustrative example, it is determinedif the VRU's real time heart rate falls below or above the VRU's restingthreshold heart rate value.

If it is determined that the VRU biosignal parameter(s) are less thanthe VRU's resting threshold value(s) (at block 406), at block 408, themethod includes determining that the context of the VRU is the passengercontext. Despite the determination that the VRU's velocity is greaterthan the VRU's running velocity threshold (at block 404), in some cases,the one or more real time biosignal parameters associated with the VRU104 fall below the resting threshold value(s) (as determined at block406). Therefore, the VRU context determination module 146 determinesthat the VRU 104 is not in a high state of physical activity that wouldjustify the context of the VRU 104 as the biking context. In otherwords, since one or more of the VRU's biosignal parameters aredetermined to be below the resting threshold value(s), the VRU 104 isdetermined to be in a non-active state, and therefore, the VRU 104 isnot determined to be biking even though his/her velocity is greater thanthe running velocity threshold value (at block 404). Therefore, the VRUcontext determination module 146 can determine that the context of theVRU 104 is the passenger context.

As an illustrative example, it is determined if the VRU's real timevelocity is greater than the running velocity threshold value when oneor more of the VRU's biosignal parameters are below the VRU's restingthreshold. Since the VRU 104 is traveling at a high rate of speed, buthas a low heart rate, the VRU 104 is not determined to be biking on theroad. Instead, the VRU 104 may be a passenger within a bus who isexhibiting a resting heart rate as he or she is sitting, while the VRU104 is exhibiting a higher velocity as the bus is traveling on the road.

If it is determined that the VRU's velocity is less than the VRU'srunning velocity threshold value (at block 404) or if it is determinedthat the VRU biosignal parameter(s) is greater than the VRU's restingthreshold (at block 406), at block 410, the method includes determiningif the VRU biosignal parameter(s) is greater than the VRU's hyper activeexercise threshold value(s). Specifically, the VRU context determinationmodule 146 can communicate with the VRU bio-movement learning module 144to receive the VRU's hyper active exercise threshold value(s). Uponreceiving the VRU's hyper active threshold value(s), the VRU contextdetermination module 146 can evaluate one or more real time biosignalparameters associated with the VRU 104 to determine if they are greaterthan the VRU's hyperactive threshold value(s) determined within the VRUcontext learning phase of the VVCM application 102. As an illustrativeexample, it is determined if the VRU's real time heart rate falls belowor above the VRU's hyper active exercise threshold heart rate value.

If it is determined that the VRU biosignal parameter(s) is greater thanthe VRU's hyper active exercise threshold value(s) (at block 410), atblock 412, the method includes determining that the context of the VRU104 is the biking context and determining a biking context value. Inparticular, since the VRU's velocity is greater than the VRU's runningvelocity threshold value (at block 404), the VRU's biosignal parametersare determined to be greater than the VRU's resting threshold value(s)and hyperactive exercise threshold value(s) (at blocks 406 and 410), theVRU context determination module 146 can determine that the context ofthe VRU 104 is the biking context. Specifically, the VRU contextdetermination module 146 can determine that the VRU 104 is within thebiking context, and is therefore biking on the road. In one embodiment,the VRU context determination module 146 can further evaluate the realtime velocity data of the VRU 104 in order to assign a biking contextvalue that can be utilized by the VVCM application 102 to moreaccurately estimate the probability of collision between the VRU 104 andthe surrounding vehicle 106. For example, the biking context value canbe a measure of the velocity of the VRU 104 within the biking context(e.g., 1 to 10 value that is associated with the VRU's biking speedand/or other factors).

If it is determined that the VRU biosignal parameter(s) is less than theVRU's hyper active exercise threshold value(s) (at block 410), at block414, the method includes determining if the VRU's velocity is greaterthan the VRU's walking velocity threshold value. In particular, if it isdetermined that one or more of the VRU biosignal parameters are greaterthan the VRU's resting threshold value(s) (at block 406), but the one ormore biosignal parameters are less than the VRU's hyper active thresholdvalue(s) (at block 410), the VRU context determination module 146 takesinto account that the VRU's biosignal parameters fall in between theVRU's resting threshold value(s) and the VRU's active thresholdvalue(s). In other words, the VRU context determination module 146 cantake into account that the VRU's biosignal parameters fall below theVRU's active threshold value(s). Therefore, the VRU contextdetermination module 146 can evaluate the VRU's velocity to determine ifthe VRU 104 is in a walking context or a running context. Specifically,the VRU context determination module 146 can communicate with the VRUbio-movement learning module 144 to receive the VRU's walking velocitythreshold value. Upon receiving the VRU's walking velocity thresholdvalue, the VRU context determination module 146 can evaluate the realtime velocity of the VRU 104 to determine if it is greater than theVRU's walking velocity threshold.

If it is determined that the VRU's velocity is less than the VRU'swalking velocity threshold value (at block 414), at block 416, themethod includes determining that the context of the VRU 104 is thewalking context and determining a walking context value. In an exemplaryembodiment, the VRU context determination module 146 can determine thatthe VRU 104 is within the walking context, and is therefore walking onthe road. In one embodiment, the VRU context determination module 146can further evaluate the real time velocity data of the VRU 104 in orderto assign the walking context value that can be utilized by the VVCMapplication 102 to more accurately estimate the probability of collisionbetween the VRU 104 and the surrounding vehicle 106. For example, thewalking context value can be a measure of the velocity of the VRU 104within the walking context (e.g., 1 to 10 value that is associated withthe VRU's walking speed and/or other factors).

If it is determined that the VRU's velocity is greater than the VRU'swalking velocity threshold value (at block 414), at block 418, themethod includes determining that the context of the VRU 104 is therunning context and determining a running context value. In an exemplaryembodiment, the VRU context determination module 146 can determine thatthe VRU 104 is within the running context, and is therefore is runningon the road.

In one embodiment, the VRU context determination module 146 can furtherevaluate the real time velocity data of the VRU 104 in order to assign arunning context value that can be utilized by the VVCM application 102to accurately estimate the probability of collision between the VRU 104and the surrounding vehicle 106. For example, the running context valuecan be a measure of the velocity of the VRU 104 within the runningcontext (e.g., 1 to 10 value that is associated with the VRU's walkingspeed and/or other factors).

In an alternate embodiment, the VRU context determination module 146 canreceive data from the physical signal sensors 132 of the wearablecomputing device 110 and/or the physical signal sensors 142 of theportable device 112 to determine if the VRU's acceleration is rhythmic.If the wearable computing device 110 and/or the portable device 112 ismoving without rhythmic acceleration, it is more likely that the VRU 104is within the biking context. Therefore, if the VRU's acceleration isrhythmic, the VRU context determination module 146 can determine if theVRU context is not the biking context Specifically, the VRU contextdetermination module 146 can utilize the physical signal sensors 132,142 to further determine the VRU's velocity in order to determine if theVRU 104 is within the walking context or the running context or withinthe passenger context. It is to be appreciated that VRU contextdetermination module 146 can also determine the VRU's context byutilizing various alternate types of data that is provided by thephysical signal sensors 132, 142 and/or the biosignal sensors 130.

In some embodiments, the VRU context determination module 146 cancommunicate with the control unit 122 of the wearable computing device110 and/or the control unit 134 of the portable device 112 to determineone or more applications that are being executed. The one or moreexecuted applications can be evaluated to determine if the applicationspertain to the context of the VRU 104. For example, the VRU 104 may usea running tracker application while running to track the VRU's runningdistance. The VRU context determination module 146 can use thisinformation to determine the context of the VRU 104 in addition to theVRU's exercise threshold values and velocity threshold values.

Referring again to FIG. 2, upon determining a context of the VRU 104 (atblock 204), as discussed in detail above, at block 206, the methodincludes determining physical movement data of a vehicle 106. In anexemplary embodiment, the vehicle physical movement determination module148 can access the vehicle sensors 118 to determine the real timephysical movement data of the vehicle 106. The vehicle physical movementdetermination module 148 can evaluate data provided by the vehiclesensors 118 (e.g., speed, yaw rate, acceleration, steering wheel angle,GNSS coordinates, etc.) in order to determine the velocity of thevehicle 106, the directional orientation of the vehicle 106, and thepositional location of the vehicle 106. In some embodiments, the vehiclephysical movement determination module 148 can determine the specificmapped location of the vehicle 106. Further, the vehicle physicalmovement determination module 148 can track the vehicle's path of travelon the road that can be saved within the storage unit 116 of the vehicle106 for a predetermined amount of time. As described in more detailbelow, the vehicle's path of travel can be utilized to more accuratelyestimate the probability of collision between the vehicle 106 and theVRU 104.

At block 208, the method includes estimating a probability of collisionbetween the VRU 104 and the vehicle 106. In an exemplary embodiment,upon the vehicle physical movement determination module 148 determiningthe physical movement data of the vehicle 106 (at block 206), thecollision probability estimation module 150 can aggregate the context ofthe VRU 104, one or more physical movement parameters associated withthe vehicle 106, and/or one or more physical movement parametersassociated with the VRU 104 in order to estimate the probability ofcollision between the vehicle 106 and the VRU 104. It is to beappreciated that the VVCM application 102 can estimate the collisionprobability at one or all of the head unit 108 of the vehicle 106, thewearable computing device 110, the portable device 112, and/or theexternally hosted computing infrastructure. It is also to be appreciatedthat one or all of the vehicle 106, the wearable computing device 110,and/or the portable device 112 can communicate between each other andthe externally hosted computing infrastructure to utilize the data to becompiled.

FIG. 5A is a process flow diagram of a method 500 for determining anoverlap between future expected positions of the VRU 104 and the vehicle106 during an execution phase of the VVCM application 102 from theoperating environment of FIG. 1 according to an embodiment. FIG. 5A willbe described with reference to the components of FIG. 1, though it is tobe appreciated that the method of FIG. 5A can be used with othersystems/components. At block 502, the method includes determining if avehicle 106 is located within a predetermined distance of the VRU 104.In one embodiment, the collision probability estimation module 150 ofthe VVCM application 102 can utilize the communication device 124 of thewearable computing device 110 and/or the communication device 136 of theportable device 112 to transmit a polling signal within a predetermineddistance from the VRU 104 to identify and communicate with one or morevehicles 106 that are located within the surrounding environment of theVRU 104.

Upon the receipt of the polling signal by the communication device 120of the vehicle 106, the collision probability estimation module 150 canutilize the communication device 120 to transmit a confirmation signalto the wearable computing device 110 and/or the portable device 112.Upon receipt of the confirmation signal by the communication device 124of the wearable computing device 110 and/or the communication device 136of the portable device 112, the collision probability estimation module150 can determine that the vehicle 106 is located within thepredetermined distance of the VRU 104. It is to be appreciated that inanother embodiment, the polling signal can be transmitted by thecommunication device 120 of the vehicle 106 to the wearable computingdevice 110 and/or the portable device 112. Accordingly, upon receipt ofthe polling signal by the communication device 124 of the wearablecomputing device 110 and/or the communication device 136 of the portabledevice 112, the confirmation signal can be transmitted to the vehicle106.

In another embodiment, the collision probability estimation module 150can access the physical signal sensors 132 of the wearable computingdevice 110 and/or the physical signal sensors 142 of the portable device112 to receive one or more real time physical movement parametersassociated with the VRU 104. Additionally, the collision probabilityestimation module 150 can access the vehicle sensors 118 to receive oneor more real time physical parameters associated with the vehicle 106.Upon receiving the one or more real time physical movement parameters,the collision probability estimation module 150 can evaluate theparameters to determine the real time positional location of the VRU 104and the real time positional location of the vehicle 106 to determine ifthe vehicle 106 and the VRU 104 are located within a predetermineddistance of the VRU 104.

Upon determining that the vehicle is located within a predetermineddistance of the VRU 104 (at block 502), at block 504, the methodincludes initiating a vehicle to VRU network. In one embodiment, thecollision probability estimation module 150 can initiate the vehicle toVRU network by using a medium such as DSRC that can be used to providedata transfer to send/receive electronic signals with the wearablecomputing device 110, the portable device 112 and the vehicle 106.

At block 506, the method includes determining the positional location ofthe VRU 104. Specifically, the collision probability estimation module150 can access the physical signal sensors 132 of the wearable computingdevice 110 and/or the physical signal sensors 142 of the portable device112 to provide the VRU's real time physical movement parameters. In oneembodiment, the collision probability estimation module 150 can alsostore the VRU's physical movement parameters within the storage unit 126of the wearable computing device 110 and/or the storage unit 138 of theportable device 112 for a predetermined period of time to establish atrend in the VRU's physical movement parameters. As will be describedbelow, the trend in the VRU's physical movement parameters can beevaluated to estimate the future position of the VRU 104. The collisionprobability estimation module 150 can further evaluate the physicalmovement parameters associated with the VRU 104 to determine thepositional location of the VRU 104.

At block 508, the method includes evaluating the directional orientationof the VRU 104. Specifically, the collision probability estimationmodule 150 can evaluate the physical movement parameters associated withthe VRU 104 to extract the directional orientation of the VRU 104 on theroad.

At block 510, the method includes estimating future positions of the VRU104. In an exemplary embodiment, the collision probability estimationmodule 150 can evaluate the physical location of the VRU 104 todetermine the exact location of the VRU 104 on the road (e.g., GNSScoordinates). Additionally, the collision probability estimation module150 can evaluate the directional orientation of the VRU 104 to determinethe heading of the VRU 104 as the VRU 104 is traveling at a specificlocation on the road. The collision probability estimation module 150can further evaluate the trend of the directional orientation andpositional location of the VRU 104 to determine a path of travel thatcan be utilized to estimate the future positions of the VRU 104 on theroad. In some embodiments, the collision probability estimation module150 can further access navigational map data (e.g., from a navigationapplication executed on the head unit 108, wearable computing device110, and/or portable device 112) to determine characteristics (e.g.,width, length, number of lanes, curbs, intersections, objects, speedlimits, etc.) of the road that the VRU 104 is traveling on. Thisinformation can also be evaluated to estimate the future position of theVRU 104.

FIG. 5B is an illustrative example of estimating an overlap between theexpected path of a VRU 104 and the expected path of a vehicle 106 basedon the process flow diagram of FIG. 5A according to an exemplaryembodiment. The collision probability estimation module 150 can evaluatethe positional location of the VRU 104 (shown as t0) and the directionalorientation of the VRU 104 (as represented by the arrow from t0). Thecollision probability estimation module 150 can also evaluate the mapdata to determine that the intersection 550 is located ahead of the VRU104 and that the directional orientation of the VRU 104 is facingtowards the intersection 550. The map data can also be evaluated todetermine that there are no objects, curves, or other outlets that couldbe possible traveled by the VRU 104 to change his/her directionalorientation. The collision probability estimation module 150 canaggregate the gathered data and can further evaluate the past trend ofthe directional orientation and positional location of the VRU 104(shown as p-3, p-2, p-1) at time t-3, t-2, t-1 to estimate the futureestimated positions of the VRU 104 (shown as p1, p2, p3) at time t1, t2,t3. As will be discussed below, the future estimated positions of theVRU 104 will be compared to the future estimated positions of thevehicle 106 in order to determine an overlap between the VRU 104 and thevehicle 106.

Referring again to FIG. 5A, at block 512, the method includesdetermining the positional location of the vehicle 106. Specifically,the collision probability estimation module 150 can utilize the vehiclesensors 118 to provide one or more real time physical movementparameters associated with the vehicle 106. In one embodiment, thecollision probability estimation module 150 can also store the one ormore physical movement parameters associated with the vehicle 106 withinthe storage unit 126 of the wearable computing device 110 and/or thestorage unit 138 of the portable device 112 for a predetermined periodof time to establish a trend of the positional location and directionalorientation of the vehicle 106. As will be described below, the trend inthe positional location and directional orientation of the vehicle 106can be evaluated to estimate the future position of the vehicle 106. Thecollision probability estimation module 150 can further evaluate thephysical movement parameters associated with the vehicle 106 todetermine the positional location of the vehicle 106.

At block 514, the method includes determining the directionalorientation of the vehicle 106. Specifically, the collision probabilityestimation module 150 can evaluate the physical movement parametersassociated with the vehicle 106 to extract the directional orientationof the vehicle 106 on the road. At block 516, the method includesestimating future positions of the vehicle 106. In an exemplaryembodiment, the collision probability estimation module 150 can evaluatethe physical location of the vehicle 106 to determine the exact locationof the vehicle 106 on the road (e.g., GNSS coordinates). Additionally,the collision probability estimation module 150 can evaluate thedirectional orientation of the vehicle 106 to determine the heading ofthe vehicle 106 as the VRU 104 is traveling at a specific location onthe road. The collision probability estimation module 150 can furtherevaluate the trend of the directional orientation and positionallocation of the VRU 104 to determine a path of travel that can beutilized to estimate the future position of the vehicle 106. Asdiscussed, in some embodiments, the collision probability estimationmodule 150 can further access the navigational map data to determinecharacteristics of the road that the vehicle 106 is traveling on to beevaluated to estimate the future position of the vehicle 106.

Referring again to the illustrative example of FIG. 5B, the collisionprobability estimation module 150 can evaluate the positional locationof the vehicle 106 (shown as x0) and the directional orientation of thevehicle 106 (as represented by the arrow from x0). The collisionprobability estimation module 150 can evaluate the map data to determinethat the intersection 550 is located ahead of the vehicle 106 and thatthe directional orientation of the vehicle 106 is facing towards theintersection 550. The map data can also be evaluated to determine thatthere are no objects, curves, or other outlets that could possibly causethe driver of the vehicle 106 to change the vehicle's directionalorientation. The collision probability estimation module 150 canaggregate the gathered data and can further evaluate the trend of thedirectional orientation and positional location of the vehicle 106(shown as x-1, x-2, x-3) to estimate the future estimated positions ofthe vehicle 106 (shown as x1, x2, x3).

Referring again to FIG. 5A, at block 518, the method includesdetermining if the future expected positions of the VRU 104 overlap withthe future expected positions of the vehicle 106. In an exemplaryembodiment, the collision probability estimation module 150 can evaluatethe estimated future positions of the VRU 104 (determined at block 510)and the estimated future positions of the vehicle 106 (determined atblock 516) to determine one or more estimated points of overlap. Asdescribed below, the collision probability estimation module 150 canregister the one or more estimated points of overlap as positionlocational coordinates that can be used to estimate the probability ofcollision between the VRU 104 and the vehicle 106. As shown in FIG. 5B,the future estimated positions of the VRU 104 will be compared to thefuture estimated positions of the vehicle 106 in order to estimate anoverlap of the estimated position of the vehicle 106 at x3 and theestimated position of the VRU at t3.

Referring again to FIG. 5A, if it is determined that the future expectedpositions of the VRU 104 overlap with the future expected positions ofthe vehicle 106 (at block 518), at block 520, the method includesestimating a probability of collision between the VRU 104 and thevehicle 106. As will be described in more detail, with respect to FIG. 6below, the collision probability estimation module 150 can evaluate thecontext of the VRU 104, the velocity of the VRU 104, the velocity of thevehicle 106, and one or more collision probability factors to estimatethe probability of collision between the VRU 104 and the vehicle 106.

FIG. 6 is a process flow diagram of a method 600 for estimating aprobability of collision between a VRU 104 and a vehicle 106 during anexecution phase of the VVCM application 102 from the operatingenvironment of FIG. 1 according to an embodiment. FIG. 6 will bedescribed with reference to the components of FIG. 1, though it is to beappreciated that the method of FIG. 6 can be used with othersystems/components. In an exemplary embodiment, once it is determinedthat the future expected positions of the VRU 104 overlap with thefuture estimated positions of the vehicle 106 (at block 516 of FIG. 5A),the collision probability estimation module 150 can evaluate additionalcollision probability factors to estimate the probability that theoverlap of estimated future positions will result in a collision betweenthe VRU 104 and the vehicle 106. In one embodiment, the probability ofcollision can include one or more values that are indicative ofpropensity and intensity of collision between the VRU 104 and thevehicle 106.

At block 602, the method includes evaluating the context of the VRU 104.In one embodiment, the collision probability estimation module 150 cancommunicate with the VRU context determination module 146 to receive thecontext of the VRU 104 that is located within a predetermined distanceof the vehicle 106. The collision probability estimation module 150 canevaluate the context of the VRU 104 to partially determine theprobability of collision between the VRU 104 and the vehicle 106. In oneembodiment, the collision probability estimation module 150 can estimatea higher probability of collision when the VRU 104 is within the bikingcontext and a relatively lower probability of collision when the VRU 104is within the running context. Additionally, the collision probabilityestimation module 150 can estimate a higher probability of collisionwhen the VRU 104 is within the running context and a relatively lowerprobability of collision when the VRU 104 is within the walking context.As an illustrative example, when the VRU 104 is within the bikingcontext the collision probability estimation module 150 may determinethat the VRU's reaction and time to slow down, stop, and/or avoid animminent collision with the vehicle 106 may be reduced. Therefore, theestimated collision probability can be increased. Also, the collisionprobability estimation module 150 can lower the probability of collisionbetween the VRU 104 and the vehicle 106 when the VRU 104 is within thepassenger context.

The collision probability estimation module 150 can also evaluate theassociated context value of the VRU 104 in order to estimate a starting,stopping, and moving rhythm of the VRU 104 to more accurately estimatethe probability of collision between the VRU 104 and the vehicle 106. Asan illustrative example, when the VRU 104 is within the running contextand is determined to have a running context value of 8 out of 10, thecollision probability estimation module 150 can determine that the VRU104 is more likely to move aggressively or randomly than if the contextvalue is 4 out of 10. In some embodiments, the collision probabilityestimation module 150 can communicate with the VRU context determinationmodule 146 to receive one or more of the VRU's real time biosignalparameters. Additionally, the collision probability estimation module150 can communicate with the VRU bio-movement learning module 144 todetermine the VRU's exercise threshold values.

The collision probability estimation module 150 can evaluate the one ormore real time biosignal parameters to determine one or more parametersthat are determined to be highly exceeding the VRU's hyper activeexercise threshold value in order to increase the probability ofcollision between the VRU 104 and the vehicle 106. For instance, if theheart rate or respiratory rate of the VRU 104 is determined to be veryhigh compared to the VRU's hyper active exercise threshold value, thecollision probability estimation module 150 can increase the probabilityof collision between the VRU 104 and the vehicle 106, as the VRU 104maybe in a state of elevated stress. It will be apparent that othercollision probability factors (e.g., eye gaze of the VRU 104, vehiclesafety system data, etc.), can be evaluated by the collision probabilityestimation module 150 to estimate the probability of collision betweenthe VRU 104 and the vehicle 106.

At block 604, the method includes evaluating the real time velocity ofthe VRU 104 and the vehicle 106. Specifically, the collision probabilityestimation module 150 can access the physical signal sensors 132 of thewearable computing device 110 and/or the physical signal sensors 142 ofthe portable device 112 to receive the real time physical movementparameters associated with the VRU 104. The collision probabilityestimation module 150 can extract the VRU's real time velocity from thereal time physical movement parameters in order to evaluate the realtime velocity of the VRU 104. Similarly, the collision probabilityestimation module 150 can access the vehicle sensors 118 to receive thereal time physical movement parameters associated with the vehicle 106.In an exemplary embodiment, the collision probability estimation module150 can evaluate the real time velocity of the VRU 104 along with theestimated future positions of the VRU 104 (as determined at block 508 ofFIG. 5A) with respect to the real time velocity of the vehicle 106 alongwith the estimated future positions of the vehicle 106 (as determined atblock 514 of FIG. 5A) to determine the predicted timeframe at which theVRU 104 and the vehicle 106 can collide. The predicted timeframe can beutilized by the collision probability estimation module 150 to estimatethe probability of collision between the VRU 104 and the vehicle 106.

At block 608, it is determined if eye gaze data is available. In onemore embodiments, the physical signal sensors 132 of the wearablecomputing device 110 can include one or more cameras and/or eye gazesensors that can determine the eye gaze of the VRU 104. For example, thewearable computing device 110 can include a head mounted display unit orglasses that can include one or more cameras and/or eye gaze sensorsthat can be utilized by various applications that are executed oraccessed by the wearable computing device 110. The collision probabilityestimation module 150 can access the physical signal sensors 132 todetermine if eye gaze data is available.

If it is determined that the eye gaze data is available (at block 608),at block 610, the method includes evaluating the eye gaze data todetermine if the eye gaze of the VRU 104 is in the direction of thevehicle 106. Specifically, the collision probability estimation module150 can access the physical signal sensors 132 of the wearable computingdevice 110 to receive the real time eye gaze data of the VRU 104. Uponreceiving the eye gaze data of the VRU 104, the collision probabilityestimation module 150 can evaluate the eye gaze data to determine if theVRU's eye gaze is in the direction of the vehicle 106 (i.e., the VRU 104is looking at and/or has seen the vehicle 106). Specifically, thecollision probability estimation module 150 can analyze the gaze dataalong with the physical location and directional orientation of the VRU104 and the vehicle 106 in order to determine if the gaze of the VRU 104is in the direction of the physical location of the vehicle 106. Thegaze data can be utilized by the collision probability estimation module150 as another collision probability factor to estimate the probabilityof collision between the VRU 104 and the vehicle 106. For example, ifthe collision probability estimation module 150 determines that the VRU104 is looking at the vehicle 106, the collision probability estimationmodule 150 can lower the probability of collision between the VRU 104and the vehicle 106, as the VRU 104 may account for the vehicle 104 andchange his/her course of travel.

At block 612, it is determined if vehicle safety system data isavailable. In one more embodiments, the vehicle sensors 118 can include,but are not limited to, cameras mounted to the interior or exterior ofthe vehicle 106, radar and laser sensors mounted to the exterior of thevehicle 106, and/or radar and laser sensors. These sensors can be usedby one or more vehicles safety systems (e.g., blind spot sensing system,park assist system, collision avoidance system, etc.) that can be usedto warn the driver of the vehicle 106 of one or more potential safetyhazards. The collision probability estimation module 150 can access thevehicle sensors 118 to determine if vehicle safety system data isavailable.

If it is determined that the vehicle safety system data is available (atblock 612), at block 614, the method includes evaluating the vehiclesafety system data to determine if the VRU 104 is sensed. Specifically,the collision probability estimation module 150 can access the vehiclesensors 118 to receive real time safety system data. Upon receiving thesafety system data, the collision probability estimation module 150 canevaluate the safety system data to determine if the VRU 104 is detectedby the safety system data (i.e., the driver of the vehicle 106 isprovided a warning or notification of the presence and/or location ofthe VRU 104). In one embodiment, the collision probability estimationmodule 150 can communicate with the head unit 108 of the vehicle 106 todetermine if one or more vehicle safety systems provide an indication orwarning to the driver of the vehicle 106 of the presence and/or locationof the VRU 104. The vehicle safety system data can be utilized by thecollision probability estimation module 150 as another collisionprobability factor to estimate the probability of collision between theVRU 104 and the vehicle 106. For example, if the collision probabilityestimation module 150 determines that the driver of the vehicle 106 hasbeen notified of the presence of the VRU 104 on the road, the collisionprobability estimation module 150 can lower the probability of collisionbetween the VRU 104 and the vehicle 106, as the driver of the vehicle106 may account for the VRU 104 and change the vehicle's course oftravel.

At block 616, it is determined if VRU profile data is available. Asdiscussed above, the storage unit 138 of the portable device 112 caninclude profile data that pertains to the VRU 104 that is utilized byone or more applications that are executed on the portable device 112.Such profile data could have been inputted during an initial setup ofthe portable device 112 and can include, but is not limited to, theuser's age, gender, and/or other demographic information. The collisionprobability estimation module 150 can access the storage unit 138 todetermine if profile data is available.

If it is determined that VRU profile data is available (at block 616),at block 618, the method includes evaluating profile data to determinethe VRU's propensity of causing a collision. In one embodiment, thecollision probability estimation module 150 can access the profile datafrom the storage unit 138 to access demographic information pertainingto the VRU 104 that includes. For instance, the collision probabilityestimation module 150 can characterize various VRU age groups as havinga higher propensity to be involved in a collision. The profile data canbe utilized by the collision probability estimation module 150 asanother collision probability factor to estimate the probability ofcollision between the VRU 104 and the vehicle 106. For example, if theVRU's age group is characterized as a child (specifically, a boy), thecollision probability estimation module 150 may increase the probabilityof collision between the VRU 104 and the vehicle 106, as a child may notexhibit much caution and may perform more irrational movements thatequate to a higher propensity of being involved in a collision.

At block 620, the method includes determining if VRU application usagedata is available. The collision probability estimation module 150 cancommunicate with the control unit 122 of the wearable computing device110 and/or the control unit 134 of the portable device 112 to access theapplication usage data in order to determine one or more specificapplications that are being used by the VRU 104 via the wearablecomputing device 110 and/or the portable device 112. The collisionprobability estimation module 150 can access the control unit 122 and/orthe control unit 134 to determine if the VRU application usage data isavailable.

If it is determined that the VRU application usage data is available (atblock 620), at block 622, the method includes evaluating the applicationusage data to determine the VRU's propensity of causing a collision.Specifically the collision probability estimation module 150 evaluatesthe application usage data in order to determine if the VRU 104 isutilizing certain types of applications (e.g., texting, social media,gaming, etc.) that require the VRU 104 to divert his or her attentionfrom the road. The VRU application usage data can be utilized by thecollision probability estimation module 150 as another collisionprobability factor to estimate the probability of collision between theVRU 104 and the vehicle 106. For example, if the VRU 104 is determinedto be using a social media application, the collision probabilityestimation module 150 may increase the probability of collision betweenthe VRU 104 and the vehicle 106, as the VRU 104 may be distracted.

At block 624, the method includes estimating the probability ofcollision between the VRU 104 and the vehicle 106. In an exemplaryembodiment, the collision probability estimation module 150 canestablish a collision probability range to specify the potential timeframe and intensity of the probable collision between the VRU 104 andthe vehicle 106. In one embodiment, the collision probability range canbe divided into ten subunits, wherein a lower probability of collisioncan be represented as a value of 1 and an extremely high probability ofcollision can be represented as a value of 10. It is to be appreciatedthat collision probability estimation module 150 can estimate theprobability of collision to be represented in various types formats suchas of different ranges, metrics, and values. As the VRU 104 and thevehicle 106 are traveling and approach each other the method 600 cancontinuously be executed to re-estimate the probability of collisionbetween the VRU 104 and the vehicle 106 in order to adjust theprobability of collision.

Referring again to FIG. 2, upon estimating a probability of collisionbetween the VRU 104 and the vehicle 106 (at block 208), as discussed indetail above, at block 210, the method includes controlling a HMI outputresponse. In an exemplary embodiment, the HMI output control module 152of the VVCM application 102 can communicate with the collisionprobability estimation module 150 to receive the estimated probabilityof collision. Upon receiving the estimated probability of collision, theHMI output control module 152 can access the head unit 108 of thevehicle 106 to provide one or more collision prevention warnings to thedriver of the vehicle 106. Additionally, the HMI output control module152 can access the HMI output unit 128 of the wearable computing device110 and/or the HMI output unit 140 of the portable device 112 to provideone or more collision prevention warnings to the VRU 104. The one ormore collision prevention warnings can be presented to the driver of thevehicle 106 and/or the VRU 104 as an audio warning, a visual warning,and/or a haptic warning. For example, upon receiving the estimatedprobability of collision, the HMI output control module 152 can providevisual warning via the display of the head unit 108 and/or the portabledevice 112 and an audio warning via the audio system of the vehicle 106and/or speakers of the portable device 112.

In one or more embodiments, the one or more collision preventionwarnings provided to the driver of the vehicle 106 and/or the VRU 104can vary in intensity based on the estimated probability of collisionbetween the VRU 104 and the vehicle 106, as estimated by the collisionprobability estimation module 150. For instance, the HMI output controlmodule 152 may evaluate the collision probability range to determine ifthe probability of collision is a lower, medium, or high probability ofcollision. The HMI output control module 152 can then provide the one ormore collision prevention warnings at a level that corresponds to theestimated collision probability range value. For example, the lowintensity warning (indicative of a low probability of collision) caninclude a simple audio buzzing warning that is presented via the audiosystem of the vehicle 106 and/or speakers of the portable device 112.Additionally, the high intensity warning (indicative of a highprobability of collision) can include tactile feedback via the steeringwheel of the vehicle 106 and the portable device 112 along with audioand visual warnings that are provided to the driver of the vehicle 106and/or the VRU 104.

In some embodiments, upon receiving the estimated probability ofcollision, the HMI output control module 152 can access the VCD 114 inorder to control one or more vehicle systems and/or functions (e.g.,engine control unit, acceleration, braking, etc.) to provide thecollision avoidance capability. For example, the VCD 114 can control theengine control unit and/or the braking system to decelerate the speed ofthe vehicle 106 or stop the vehicle 106 based on a estimated probabilityof collision between the vehicle 106 and the VRU 104. The collisionavoidance capability can be based on the estimated probability ofcollision between the VRU 104 and the vehicle 106, as estimated by thecollision probability estimation module 150. For instance, the VCD 114may evaluate the collision probability range to determine if theprobability of collision is a lower, medium, or high probability ofcollision. The VCD 114 can then control one or more vehiclesystems/functions in a manner that corresponds to the estimatedcollision probability range value.

The embodiments discussed herein may also be described and implementedin the context of non-transitory computer-readable storage mediumstoring computer-executable instructions. Non-transitorycomputer-readable storage media includes computer storage media andcommunication media. For example, flash memory drives, digital versatilediscs (DVDs), compact discs (CDs), floppy disks, and tape cassettes.Non-transitory computer-readable storage media may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer readableinstructions, data structures, modules or other data. Non-transitorycomputer readable storage media excludes transitory and propagated datasignals.

It will be appreciated that various implementations of theabove-disclosed and other features and functions, or alternatives orvarieties thereof, may be desirably combined into many other differentsystems or applications. Also that various presently unforeseen orunanticipated alternatives, modifications, variations or improvementstherein may be subsequently made by those skilled in the art which arealso intended to be encompassed by the following claims.

The invention claimed is:
 1. A computer-implemented method for vehiclecollision mitigation with vulnerable road user context sensing,comprising: determining one or more biosignal parameters and one or morephysical movement parameters associated with a vulnerable road user(VRU), wherein determining the one or more biosignal parametersassociated with the VRU includes biosignal sensors sensing physiologicaldata, wherein determining the one or more physical movement parametersassociated with the VRU includes physical signal sensors providing oneor more physical movement parameters, wherein determining the one ormore biosignal parameters and the one or more physical movementparameters associated with the VRU includes at least one of: a wearablecomputing device sensing one or more biosignal parameters associatedwith the VRU, the wearable computing device sensing one or more physicalmovement parameters associated with the VRU, and a portable devicesensing one or more physical movement parameters associated with theVRU, wherein the one or more biosignal parameters and the one or morephysical movement parameters are sensed and stored for a predeterminedamount of time on at least one of: a storage unit of the wearablecomputing device and a storage unit of the portable device; determiningexercise threshold values of the VRU based on an aggregation of the oneor more biosignal parameters and velocity data that is extracted fromthe one or more physical movement parameters and velocity thresholdvalues of the VRU based on average ranges of the VRU's velocity that isdetermined from the velocity data that is extracted from the one or morephysical movement parameters, wherein the velocity threshold valuesinclude at least one of: a resting velocity threshold value, a walkingvelocity threshold value, and a running velocity threshold value,wherein the velocity threshold values of the VRU are based on analyzingthe physical movement parameters associated with the VRU that are sensedand stored for the predetermined amount of time; determining a contextof the VRU based on the one or more biosignal parameters and one or morephysical movement parameters associated with the VRU, whereindetermining the context of the VRU includes determining if the one ormore biosignal parameters are greater than the exercise threshold valuesand determining if the one or more physical movement parameters aregreater than the velocity threshold values of the VRU, wherein thecontext of the VRU can include at least one of: a walking context, arunning context, a biking context, and a passenger context that aredetermined based on determining if the one or more biosignal parametersare greater than one or more exercise threshold values that aresubjectively associated with the VRU and determining if the one or morephysical movement parameters are greater than the velocity thresholdvalues that are subjectively associated with the VRU; determining one ormore physical movement parameters associated with a vehicle; estimatinga probability of collision between the VRU and the vehicle based on thecontext of the VRU, the one or more physical movement parametersassociated with the VRU, and the one or more physical movementparameters associated with the vehicle; and providing a human machineinterface output response based on the probability of collision betweenthe VRU and the vehicle, wherein the human machine interface outputresponse is provided on at least one of the following: a head unit ofthe vehicle, a wearable computing device, and a portable device.
 2. Themethod of claim 1, wherein the exercise threshold values include atleast one of: a resting exercise threshold value, an active exercisethreshold value, and a hyperactive exercise threshold value, wherein theexercise threshold values of the VRU are based on analyzing the one ormore biosignal parameters associated with the VRU that are sensed andstored for the predetermined amount of time.
 3. The method of claim 1,wherein estimating the probability of collision between the VRU and thevehicle includes determining an overlap between future expectedpositions of the VRU and future expected positions of the vehicle,wherein the future expected positions of the VRU are determined byanalyzing one or more physical movement parameters associated with theVRU in real time, wherein the future expected positions of the vehicleis determined by analyzing one or more physical movement parametersassociated with the vehicle in real time.
 4. The method of claim 3,wherein estimating the probability of collision between the VRU and thevehicle includes evaluating the context of the VRU, a velocity of theVRU in real time, and a velocity of the vehicle in a real time withrespect to the determined overlap between the future expected positionsof the VRU and the future positions of the vehicle.
 5. The method ofclaim 3, wherein estimating the probability of collision between the VRUand the vehicle includes evaluating one or more collision probabilityfactors to determine at least one of: a probability that the overlap ofthe future expected positions of the VRU and the future expectedpositions of the vehicle will result in a collision and a predictedtimeframe at which the VRU and the vehicle may collide.
 6. The method ofclaim 1, wherein providing the human machine interface output responseincludes controlling a human machine interface to provide an outputresponse that corresponds to the estimated probability of collisionbetween the VRU and the vehicle.
 7. A system for providing vehiclecollision mitigation with vulnerable road user context sensing,comprising: a VRU vehicle collision mitigation application that isexecuted on at least one of: a wearable computing device worn by and/orin possession of a vulnerable road user (VRU), a portable device inpossession of the VRU, and a head unit of a vehicle; wherein thewearable computing device includes biosignal sensors and physical signalsensors, the portable device includes physical signal sensors, and thevehicle includes vehicle sensors; a VRU bio-movement learning modulethat is included as a module of the VRU vehicle collision mitigationapplication that determines one or more biosignal parameters and one ormore physical movement parameters associated with the VRU, whereindetermining the one or more biosignal parameters associated with the VRUincludes the biosignal sensors sensing physiological data, whereindetermining the one or more physical movement parameters associated withthe VRU includes the physical signal sensors providing one or morephysical movement parameters, wherein determining the one or morebiosignal parameters and the one or more physical movement parametersassociated with the VRU includes at least one of: the wearable computingdevice sensing one or more biosignal parameters associated with the VRU,the wearable computing device sensing one or more physical movementparameters associated with the VRU, and the portable device sensing oneor more physical movement parameters associated with the VRU, whereinthe one or more biosignal parameters and the one or more physicalmovement parameters are sensed and stored for a predetermined amount oftime on at least one of: a storage unit of the wearable computing deviceand a storage unit of the portable device, wherein determining the oneor more biosignal parameters associated with the VRU includesdetermining exercise threshold values of the VRU based on an aggregationof the one or more biosignal parameters and velocity data that isextracted from the one or more physical movement parameters and velocitythreshold values of the VRU based on average ranges of the VRU'svelocity that is determined from the velocity data that is extractedfrom the one or more physical movement parameters, wherein the velocitythreshold values include at least one of: a resting velocity thresholdvalue, a walking velocity threshold value, and a running velocitythreshold value, wherein the velocity threshold values of the VRU arebased on analyzing the physical movement parameters associated with theVRU that are sensed and stored for the predetermined amount of time; aVRU context determination module that is included as a module of the VRUvehicle collision mitigation application that determines a context ofthe VRU based on the one or more biosignal parameters and the physicalmovement parameters associated with the VRU, wherein determining thecontext of the VRU includes determining if the one or more biosignalparameters are greater than the exercise threshold values anddetermining if the one or more physical movement parameters are greaterthan the velocity threshold values of the VRU, wherein the context ofthe VRU can include at least one of: a walking context, a runningcontext, a biking context, and a passenger context that are determinedbased on determining if the one or more biosignal parameters are greaterthan one or more exercise threshold values that are subjectivelyassociated with the VRU and determining if the one or more physicalmovement parameters are greater than the velocity threshold values thatare subjectively associated with the VRU; a vehicle physical movementdetermination module that is included as a module of the VRU vehiclecollision mitigation application that determines one or more physicalmovement parameters associated with the vehicle; a collision probabilityestimation module that is included as a module of the VRU vehiclecollision mitigation application that estimates a probability ofcollision between the VRU and the vehicle based on the context of theVRU, the one or more physical movement parameters associated with theVRU, and the one or more physical movement parameters associated withthe vehicle; and a HMI control module that is included as a module ofthe VRU vehicle collision mitigation application that provides a humanmachine interface output response based on the probability of collisionbetween the VRU and the vehicle, wherein the human machine interfaceoutput response is provided on at least one of the following: the headunit of the vehicle, the wearable computing device, and the portabledevice.
 8. The system of claim 7, wherein the exercise threshold valuesinclude at least one of: a resting exercise threshold value, an activeexercise threshold value, and a hyperactive exercise threshold value,wherein the exercise threshold values of the VRU are based on analyzingthe one or more biosignal parameters associated with the VRU that aresensed and stored for the predetermined amount of time.
 9. The system ofclaim 7, wherein the collision probability estimation module determinesan overlap between future expected positions of the VRU and futureexpected positions of the vehicle, wherein the future expected positionsof the VRU are determined by analyzing one or more physical movementparameters associated with the VRU in real time, wherein the futureexpected positions of the vehicle are determined by analyzing one ormore physical movement parameters associated with the vehicle in realtime.
 10. The system of claim 9, wherein the collision probabilityestimation module evaluates the context of the VRU, a velocity of theVRU in real time, and a velocity of the vehicle in real time withrespect to the determined overlap between the future expected positionsof the VRU and the future positions of the vehicle.
 11. The system ofclaim 9, wherein the collision probability estimation module evaluatesone or more collision probability factors to determine a probabilitythat the overlap of the future expected positions of the VRU and thefuture expected positions of the vehicle will result in a collision anda predicted timeframe at which the VRU and the vehicle may collide. 12.The system of claim 7, wherein the HMI control module controls a humanmachine interface to provide an output response that corresponds to theportability of collision between the VRU and the vehicle.